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arXiv · 2211.15202

Revisiting Distance Metric Learning for Few-Shot Natural Language Classification

Witold Sosnowski·Anna Wróblewska·Karolina Seweryn·Piotr Gawrysiak
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Abstract

Distance Metric Learning (DML) has attracted much attention in image processing in recent years. This paper analyzes its impact on supervised fine-tuning language models for Natural Language Processing (NLP) classification tasks under few-shot learning settings. We investigated several DML loss functions in training RoBERTa language models on known SentEval Transfer Tasks datasets. We also analyzed the possibility of using proxy-based DML losses during model inference. Our systematic experiments have shown that under few-shot learning settings, particularly proxy-based DML losses can positively affect the fine-tuning and inference of a supervised language model. Models tuned with a combination of CCE (categorical cross-entropy loss) and ProxyAnchor Loss have, on average, the best performance and outperform models with only CCE by about 3.27 percentage points -- up to 10.38 percentage points depending on the training dataset.

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Witold Sosnowski, Anna Wróblewska, Karolina Seweryn, Piotr Gawrysiak. 2022-11-28. Revisiting Distance Metric Learning for Few-Shot Natural Language Classification. https://arxiv.org/abs/2211.15202

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